Skip to main content

PyCon: How the PyPy JIT Works

See the website.

"If the implementation is hard to explain, it's a bad idea." (Except PyPy!)

The JIT is interpreter agnostic.

It's a tracing JIT. They compile only the code that's run repeatedly through the interpreter.

They have to remove all the indirection that's there because it's a dynamic language.

They try to optimize simple, idiomatic Python. That is not an easy talk.

(The room is packed. I guess people were pretty excited about David Beazley's keynote.)

There's a metainterpreter. It traces through function calls, flattening the loop.

JIT compiler optimizations are different than compiler optimizations. You're limited by speed. You have to do the optimizations fast.

If objects are allocated in a loop and they don't escape the loop, they don't need to use the heap and they can remove boxing.

They do unrolling to take out the loop invariants.

They have a JIT viewer.

Generating assembly is surprisingly easy. They use a linear register allocator. The GC has to be informed of dynamic allocations.

They use guards that must be true to continue using the JITted code. I.e., did the code raise an exception?

They have data structures optimized for the JIT such as map dicts.

They can translate attribute access to an array in certain cases.

The JIT is generated from an RPython description of the interpreter.

The metainterpreter traces hot loops and functions.

They use optimizations that remove indirection.

They adapt to new runtime information with bridges.

They added stackless support to the JIT.

They want the JIT to help with STM.

They have Prolog and Scheme interpreters written on top of the PyPy infrastructure.

They don't do much with trying to take advantage of specific CPUs.

Comments

Popular posts from this blog

Ubuntu 20.04 on a 2015 15" MacBook Pro

I decided to give Ubuntu 20.04 a try on my 2015 15" MacBook Pro. I didn't actually install it; I just live booted from a USB thumb drive which was enough to try out everything I wanted. In summary, it's not perfect, and issues with my camera would prevent me from switching, but given the right hardware, I think it's a really viable option. The first thing I wanted to try was what would happen if I plugged in a non-HiDPI screen given that my laptop has a HiDPI screen. Without sub-pixel scaling, whatever scale rate I picked for one screen would apply to the other. However, once I turned on sub-pixel scaling, I was able to pick different scale rates for the internal and external displays. That looked ok. I tried plugging in and unplugging multiple times, and it didn't crash. I doubt it'd work with my Thunderbolt display at work, but it worked fine for my HDMI displays at home. I even plugged it into my TV, and it stuck to the 100% scaling I picked for the othe

ERNOS: Erlang Networked Operating System

I've been reading Dreaming in Code lately, and I really like it. If you're not a dreamer, you may safely skip the rest of this post ;) In Chapter 10, "Engineers and Artists", Alan Kay, John Backus, and Jaron Lanier really got me thinking. I've also been thinking a lot about Minix 3 , Erlang , and the original Lisp machine . The ideas are beginning to synthesize into something cohesive--more than just the sum of their parts. Now, I'm sure that many of these ideas have already been envisioned within Tunes.org , LLVM , Microsoft's Singularity project, or in some other place that I haven't managed to discover or fully read, but I'm going to blog them anyway. Rather than wax philosophical, let me just dump out some ideas: Start with Minix 3. It's a new microkernel, and it's meant for real use, unlike the original Minix. "This new OS is extremely small, with the part that runs in kernel mode under 4000 lines of executable code.&quo

Haskell or Erlang?

I've coded in both Erlang and Haskell. Erlang is practical, efficient, and useful. It's got a wonderful niche in the distributed world, and it has some real success stories such as CouchDB and jabber.org. Haskell is elegant and beautiful. It's been successful in various programming language competitions. I have some experience in both, but I'm thinking it's time to really commit to learning one of them on a professional level. They both have good books out now, and it's probably time I read one of those books cover to cover. My question is which? Back in 2000, Perl had established a real niche for systems administration, CGI, and text processing. The syntax wasn't exactly beautiful (unless you're into that sort of thing), but it was popular and mature. Python hadn't really become popular, nor did it really have a strong niche (at least as far as I could see). I went with Python because of its elegance, but since then, I've coded both p